The majority of health and patient data is stored today as unstructured medical text, such as medical notes, prescriptions, audio interview transcripts, and pathology and radiology reports. Identifying this information today is a manual and time-consuming process; it either requires data entry by high-skilled medical experts, or teams of developers writing custom code and rules to try and extract the information automatically. In both cases this undifferentiated heavy lifting takes material resources away from efforts to improve patient outcomes through technology.

Amazon Comprehend Medical allows developers to identify the key common types of medical information automatically, with high accuracy, and without the need for large numbers of custom rules. Comprehend Medical can identify medical conditions, anatomic terms, medications, details of medical tests, treatments and procedures. Ultimately, this richness of information may be able to one day help consumers with managing their own health, including medication management, proactively scheduling care visits, or empowering them to make informed decisions about their health and eligibility.

There are no servers to provision or manage – developers only need to provide unstructured medical text to Comprehend Medical. The service will “read” the text and then identify and return the medical information contained within it. Comprehend medical will also highlight protected health information (PHI). There are no models to train, no ML experience is required, and no data processed by the service is stored or used for training. Through the Comprehend Medical application program interface (API), these new capabilities can be integrated with existing services and health systems easily. The service is also covered under Amazon Web Services’ (AWS) HIPAA eligibility and BAA.

Unlocking this information from medical language makes a variety of common medical use cases easier and cost-effective, including: clinical decision support (e.g., getting a historical snapshot of a patient’s medical history), revenue cycle management (e.g., simplifying the time-intensive manual process of data entry), clinical trial management (e.g., by identifying and recruiting patients with certain attributes into clinical trials), building population health platforms, and helping address PHI requirements (e.g., for privacy and security assurance.)

Amazon said it is working closely with Seattle’s Fred Hutchinson Cancer Research Center to support their goals to eradicate cancer in the future. Comprehend Medical is helping to identify patients for clinical trials who may benefit from specific cancer therapies. Fred Hutch was able to evaluate millions of clinical notes to extract and index medical conditions, medications and choice of cancer therapeutic options, reducing the time to process each document from hours, to seconds.

“Curing cancer is, inherently, an issue of time,” said Matthew Trunnell, chief information officer, Fred Hutchinson Cancer Research Center. “For cancer patients and the researchers dedicated to curing them, time is the limiting resource. The process of developing clinical trials and connecting them with the right patients requires research teams to sift through and label mountains of unstructured medical record data. Amazon Comprehend Medical will reduce this time burden from hours per record to seconds. This is a vital step toward getting researchers rapid access to the information they need when they need it so they can find actionable insights to advance lifesaving therapies for patients.”

“Roche’s NAVIFY decision support portfolio provides solutions that accelerate research and enable personalized healthcare. With petabytes of unstructured data being generated in hospital systems every day, our goal is to take this information and convert it into useful insights that can be efficiently accessed and understood,” said Anish Kejariwal, director of software engineering for Roche Diagnostics Information Solutions. “Amazon Comprehend Medical provides the functionality to help us with quickly extracting and structuring information from medical documents, so that we can build a comprehensive, longitudinal view of patients, and enable both decision support and population analytics.”